The ongoing 4th industrial revolution is characterized by the digitization of industrial environments, mainly based on the use of Internet of Things, Cloud Computing and Artificial Intelligence (AI). Regarding AI, although data analysis has shown to be a key enabler of industrial Cyber-Physical Systems (CPS) in the development of smart machines and products, the traditional Cloud-centric solutions are not suitable to attend the data and time-sensitive requirements. Complementary to Cloud, Edge Computing has been adopted to enable the data processing capabilities at or close to the physical components. However, defining which data analysis tasks should be deployed on Cloud and Edge computational layers is not straightforward. This work proposes a framework to guide engineers during the design phase to determine the best way to distribute the data analysis capabilities among computational layers, contributing for a lesser ad-hoc design of distributed data analysis in industrial CPS. Besides defining the guidelines to identify the data analysis requirements, the core contribution relies on a Fuzzy Logic recommendation system for suggesting the most suitable layer to deploy a given data analysis task. The proposed approach is validated in a smart machine testbed that requires the implementation of different data analysis tasks for its operation.

中文翻译：

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支持信息物理系统云边数据分析设计的模糊逻辑推荐系统


正在进行的第四次工业革命的特点是工业环境的数字化，主要基于物联网、云计算和人工智能（AI）的使用。关于人工智能，尽管数据分析已被证明是智能机器和产品开发中工业信息物理系统（CPS）的关键推动者，但传统的以云为中心的解决方案不适合满足数据和时间敏感的要求。作为云的补充，边缘计算已被采用，以实现物理组件或接近物理组件的数据处理能力。然而，定义哪些数据分析任务应部署在云和边缘计算层上并不简单。这项工作提出了一个框架，指导工程师在设计阶段确定在计算层之间分配数据分析功能的最佳方式，从而有助于工业 CPS 中分布式数据分析的较少临时设计。除了定义指南来确定数据分析需求之外，核心贡献还依赖于模糊逻辑推荐系统来建议最合适的层来部署给定的数据分析任务。所提出的方法在智能机器测试台中得到验证，该测试台需要为其操作实施不同的数据分析任务。